Data Provenance and Data Lineage: What's the Difference?
Data lineage shows how data moves and changes. Data provenance adds source history, responsibility, and evidence for evaluating trust.
Two related views of data history
Data provenance and data lineage are closely related, but they answer different questions. Data lineage maps how data moves through systems and how pipelines transform it from source to output. It helps teams understand dependencies, assess the impact of changes, troubleshoot failures, and locate where an unexpected result entered the data flow. In simple terms, lineage describes the path the data followed.
Data provenance provides broader historical and evidential context. It can record where the data originated, which processes modified it, when those changes occurred, and which people or systems were responsible. As IBM's data provenance overview explains, this additional context supports auditing, validation, accountability, and reproducibility. If lineage is the map of a data journey, provenance is the supporting record explaining what happened along that journey.
Organizations often need both. Lineage can show that a published dataset was derived from several source tables through a sequence of transformations; provenance evidence can connect those steps to exact dataset states, timestamps, transformation records, and attestations. Neither automatically proves that the original data was truthful, lawful, or high quality. Stronger assurance may still require chain-of-custody evidence, reproducible processing, governance checks, or independent review.
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See this evidence workflow on a real dataset.